Method for sensing of biometric data and use thereof for bidirectional communication with networked devices

ABSTRACT

A method of using a sensor platform of a garment of a wearer in order to interact with a remote networked device using a plurality of sensed biometric data, the method comprising: receiving from the sensors a set of the plurality of biometric data; comparing the set to a data model including a plurality of model data parameters; determining whether said comparing indicates a need for a command to be sent to the remote networked device in order to effect a change in an operational characteristic of the networked device; sending the command to the networked device; receiving a further set of the plurality of biometric data; further comparing the further set to the data model; and determining whether said further comparing indicates a need for a further command to be sent to the remote networked device in order to further effect a change in an operational characteristic of the networked device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present applicant is a continuation application of PCT Applicationno. PCT/CA2018/000136, filed on Jul. 4, 2018, which claims priority fromU.S. Provisional Patent Application No. 62/528,565, filed on Jul. 5,2017; the entire contents of which are hereby incorporated by referenceherein.

FIELD

The present disclosure relates to bidirectional sensing systems forbiometric data.

BACKGROUND

Sensing of biometric data in today's technological based environment iskey to understanding and affecting the state of a garment wearer. Inparticular, athletes and medical patients, among a number of otherconsumers, are key individuals for much needed accurate and up-to-date(i.e. real-time) biometric sensing, in order to influence (e.g. change)operational characteristics of networked devices in the vicinity of thewearer. However, state of the art sensor arrangements and methods ofdata processing are cumbersome and have limited applicability andadaptability to a wearer's varied lifestyle, including ever-changingphysical and mental states.

SUMMARY

It is an object of the present invention to provide a sensing platformand method of use thereof to obviate or mitigate at least one of theabove presented disadvantages.

An aspect provided is a method of using a sensor platform of a garmentof a wearer in order to interact with a remote networked device using aplurality of sensed biometric data, the method comprising: receivingfrom the sensors a set of the plurality of biometric data; comparing theset to a data model including a plurality of model data parameters;determining whether said comparing indicates a need for a command to besent to the remote networked device in order to effect a change in anoperational characteristic of the networked device; sending the commandto the networked device; receiving a further set of the plurality ofbiometric data; further comparing the further set to the data model; anddetermining whether said further comparing indicates a need for afurther command to be sent to the remote networked device in order tofurther effect a change in an operational characteristic of thenetworked device.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects will now be described by way of exampleonly with reference to the attached drawings, in which:

FIG. 1 is a perspective view of a band containing a plurality ofsensors;

FIG. 2 is a view of the band shown in FIG. 1 incorporated into anarticle of clothing;

FIG. 3 shows an embodiment of the band shown in FIG. 1 with associatedelectrical components;

FIG. 4 shows example applications of the biometric data combinations;

FIG. 5 shows a front perspective view of a further embodiment of theband of FIG. 1;

FIG. 6 shows a rear perspective view of the further embodiment of FIG.5;

FIG. 7 shows a side view of the sensors mounted on the band of FIG. 5;

FIGS. 8 and 9 show further embodiments of the sensors of FIG. 1;

FIG. 10 shows a block diagram of a system for processing biometric dataand acting thereon for the sensor platform shown in FIG. 1, by example;

FIG. 11 is a block diagram of an interaction service for the system ofFIG. 10; and

FIG. 12 is a flowchart of an example operation of the system of FIG. 10.

DETAILED DESCRIPTION

Referring to FIG. 1, shown is a fabric band 10, preferable having aresilient knit type, for fitting around a body part of a wearer (notshown), in order to collect different modes/types of biometric databased on the type/number of sensors 12 positioned either on or otherwiseknit/woven (e.g. embroidered) into the fabric making up the body of theband 10. It is recognized that the body part can be such as but notlimited to: waist or abdomen; limb such as a leg or arm; torso/trunk;buttocks; foot or ankle; wrist or hand; and/or head. The fabric band 10can be provided as a stand-alone article or can be combined/combinedinto an article of clothing such as but not limited to: underwear 11(see FIG. 2—such as but not limited to any type of undergarmentincluding jockey shorts, panties, undershirts, and bras); socks, limbbands (e.g. knee band); shirt (e.g. undershirt); etc. In terms ofcombined into an article of clothing (i.e. garment 11), the band 10 canbe formed as an integral component of the interlacing of the fibresmaking up the garment 11. The fabric of the body of the band 10 can becomprised of interlaced resilient fibres (e.g. stretchable naturaland/or synthetic material and/or a combination of stretchable andnon-stretchable materials).

Referring again to FIG. 1, provided as distributed about the band 10,e.g. mounted on an interior surface 111 (i.e. inward facing towards thebody of the wearer), are a series of sensors/electrodes 12 including ECGsensors 12 a, bio impedance sensors 12 b, and strain gauge sensors 12 c.It is recognized that the sensors 12 can be composed of Electroactivepolymers, or EAPs, and/or woven or knit plurality of conductive fibresconstructed in a sensor/electrode configuration (e.g. a patch). Thesensors 12 can also include a position/location sensor in order to beable to detect the physical location of the wearer (e.g. location withinor outside of their home/building).

Also positioned on the band 10, for example on an exterior surface 13(i.e. outward facing from the wearer), is series of electricalcomponents 15 including a computer device 14 (see FIG. 3) including acomputer processor 16, a memory 18 for executing stored instructions forreceiving and processing of data obtained from the sensors 12, as wellas communicating via a network interface 20 with a network 22 (e.g.Wi-Fi, Bluetooth, attached wired cable, etc.) as well as sending andreceiving electrical signals from the sensors 12. The processor 16,memory 18 and network interface 20 are mounted on a printed circuitboard 26, which is housed in a housing 24 attached to the band 10. Alsoconnected to the PCB 24 is a temperature sensor 12 d for measuring abody temperature of the wearer. Also mounted in the housing is a powersupply 28 (e.g. battery) for powering the various electrical components15 within the housing 24 as well as the sensors 12 a,b,c external to thehousing 24, connected via conductive communication pathways 30 (e.g.wires—see FIG. 1—woven into the fabric weave/knit of the band 10textile). The pathways 30 can be coupled to the sensors 12 via use of aconductive grommet, as desired. Also provided is a series of motionsensors 36 (e.g. accelerometer(s) and gyroscopes) for determiningmovements of the wearer, including posture as further described below.The sensors 12 can also be provided as speaker/microphone (e.g. forauditory signals/communication with the wearer), illumination sensors(e.g. LEDS—for visual signals/communication with the wearer) andhaptic/vibrations sensors (e.g. actuators—for motion/touchsignals./communication with the wearer).

Sensor Examples

The sensors 12 can be composed of Electroactive polymers, or EAPs, whichare polymers that exhibit a change in size or shape when stimulated byan electric field. EAPS could also exhibit a change in electrical fieldif stimulated by mechanical deformation. The most common applications ofthis type of material are in actuators and sensors. A typicalcharacteristic property of an EAP is that they will undergo deformationwhile sustaining forces. For example, EPDM rubber containing variousadditives for optimum conductivity, flexibility and ease of fabricationcan be used as a sensor 12 material for measuring electrode impedancemeasured on human skin of the wearer. Further, EAPs may be used tomeasure ECG as well as measuring deformation (i.e. expansion of thewaist and therefore breathing can be inferred from EAPs). ECG can bemeasured using surface electrodes, textile or polymer, as desired.

These electrodes 12 can be capable of recording bio potential signalssuch as ECG while for low-amplitude signals such as EEG, as coupled viapathways 30 with an active circuit of the electrical components 15within the housing 24. the ECG sensors 12 a can be used to collect andtransmit signals to the computer processor 16 reflective of the heartrate of the wearer. AS such, it is recognized that the electrodes assensors 12 can be composed of conductive yarn/fibres (e.g. knitted,woven, embroidery using conductive fibres—e.g. silver wire/threads) ofthe band 10, as desired.

In terms of bioelectrical impedance, these sensors 12 a,b and theirmeasurements can be used in analysis (BIA) via the processor 16 andmemory 18 instructions for estimating body composition, and inparticular body fat. In terms of estimating body fat, BIA actuallydetermines the electrical impedance, or opposition to the flow of anelectric current through body tissues of the wearer interposed betweenthe sensors 12 (e.g. 12 a,b), which can then be used to estimate totalbody water (TBW), which can be used to estimate fat-free body mass and,by difference with body weight, body fat.

In terms of strain sensing, these sensors 12 c can be operated as astrain gauge to take advantage of the physical property of electricalconductance and its dependence on the conductor's geometry. When theelectrical conductor 12 c is stretched within the limits of itselasticity such that it does not break or permanently deform, the sensor12 c will become narrower and longer, changes that increase itselectrical resistance end-to-end. Conversely, when the sensor 12 c iscompressed such that it does not buckle, the sensor 12 c will broadenand shorten, changes that decrease its electrical resistance end-to-end.From the measured electrical resistance of the strain gauge, via thepower 28 that is administered to the sensors 12 via the computerprocessor 16 acting on stored 18 instructions, the amount of inducedstress can be inferred. For example, a strain gauge 12 c arranged as along, thin conductive fibres in a zig-zag pattern of parallel lines suchthat a small amount of stress in the direction of the orientation of theparallel lines results in a multiplicatively larger strain measurementover the effective length of the conductor surfaces in the array ofconductive lines—and hence a multiplicatively larger change inresistance—than would be observed with a single straight-line conductivewire. In terms of location/structure of the strain gauge 12 c, thestrain gauge can be located around the circumference of the band 10. Afurther embodiment is where the strain gauge 12 c is located in aportion of the circumference, for example in a serpentine arrangement,positioned in a front 52 portion (positioned adjacent to the front ofthe wearer) of the band 10. The strain gauge 12 c can be configured forsensing in the k Ohm range.

In terms of temperature sensor 12 d, this sensor is used to measure thedynamic body temperature of the wear. For example, the temperaturesensor 12 d can be a thermistor type sensor, which is a thermallysensitive resistors whose prime function is to exhibit a large,predictable and precise change in electrical resistance when subjectedto a corresponding change in body temperature. Examples cam includeNegative Temperature Coefficient (NTC) thermistors exhibiting a decreasein electrical resistance when subjected to an increase in bodytemperature and Positive Temperature Coefficient (PTC) thermistorsexhibiting an increase in electrical resistance when subjected to anincrease in body temperature. Other temperature sensor types can includethermocouples, resistance thermometers and/or silicon bandgaptemperature sensors as desired. It is also recognized that the sensors12 can include haptic feedback sensors that can be actuated via thecomputer processor 16 in response to sensed data 44 processed onboard bythe processor 16 and/or instructions received from a third party device60 or the wearer (operator of the computer device 40) via an interface20. Another example of temperature sensors 12 d is where thermocouplescould be knitted into the band 10 fabric using textile and coupleddirectly to the body of the wearer through close proximity/contact inorder to get more accurate temperature readings.

Sensed Data and Processing

Referring again to FIGS. 2 and 3, the processor 16 (acting on stored 18instructions) can transmit the collected data 44 (in raw format and/orin preprocessed format from the sensors 12) to an external computerdevice 40 (e.g. smartphone or other desktop application) for viewingand/or further processing of the sense data. For example, the device 40application can display the sensed data 44 in a dashboard type format 46on a display 42 (or other type of GUI interface) for viewing by thewearer (or by another person other than the wearer that has beenprovided access to the data 44). For example, the sensed data 44 can beprovided in a dashboard format indicating real-time (or other selecteddynamic periodic frequency) of: body temperature for indicatingfluctuations in skin temperature; gyroscope/accelerometer measurementsfor indicating amount/degree of physical activity (i.e. via sensedmotion) of the wearer as well as contributing via gyroscope readings ofwearer posture (for example in the case where the band 10 is positionedat the waist of the wearer) as well as determined calculation of numberof calories expended; strain gauge measurements (e.g. via conductiveyarn) in order to indicate real-time breathing of the wearer as the band10 expands and contracts as well as the ability to differentiate straindegree contributing to posture angle (i.e. band and associated strainsensor 12 c with change in length as the posture of the wearer changesdue to bending at the waist—in the case of the underwear 11 example ofFIG. 2); real-time heart rate measurements based on sensed ECG datausing the sensors 12 a; and real-time hydration/body fat measurementsbased on galvanic sensing using the sensors 12 b (and optionally 12 a asfurther described below).

It is recognized that multiple sources of sensed data (e.g. temperaturesensor 12 d with activity/motion sensors 36 can be used in an algorithmstored in memory 18 to calculate calories expended based on activitycombined with body temperature). Other combinations of sensed data typescan include combinations such as but not limited to: heart rate withactivity data; heart rate with activity data with temperature; activitydata with bio impedance data; strain gauge for breathing rate datadetermination with activity data and heart rate data for determinationof exertion levels; etc. It is also realized that combinations of sensortype readings can be used by the computer processor 16 to determineexercise activity type being performed by the wearer, based on computermodels of activity type with typical sensor data, for example gradualchanges in body posture with detected lower levels of heart rate andbreathing could be indicative of a wearer practicing yoga. A furthertype of multiple sensed data usage can be for accelerometer andgyroscope data, such that both can be used or one can be used and theother discounted during determination of a selected metric of thedashboard 46. For example, in the case of the band 10 being situated atthe waist of an overweight person, the “off-vertical” reading of thegyroscope would not be indicative of a bent posture (from the vertical),rather due to the folded waistband due to body composition. As such, thedegree of gyroscope readings would be discounted from the calculation ofthe posture determination.

Referring again to FIG. 1, the location of the sensors 12 a,b are suchthat they are positioned in pairs on either side of a centerline 50, inorder to position an appropriate amount of body mass between the sensors12 a,b as well as providing an appropriate conductive path through thebody of the wearer (e.g. cross body measurement). It is also recognizedthat placement of the sensors 12 a,b are preferred in body regions wheremuscle noise (actions of muscles can introduce signal noise into theadjacent sensors 12) is minimized. As such, the sensors 12 a,b can bepositioned in the band 10 in a location for positioning adjacent to thehip and/or the kidney of the wearer in the case where the band 10 ispositioned at the waist. It is recognized that positioning the sensors12 a,b in the band 10 in order to be adjacent to either hip of thewearer, i.e. both sensors 12 a,b of the pair to one side of thecenterline 56 of the band 10, would provide for a lower signalamplitude/quality when wearer activity is subdued (e.g. resting) howeverwould also advantageously provide an increases signal quality when thewearer is active (as the presence of utilized muscle mass adjacent tothe hip region is minimal as compared to other regions about the waist).

It is also recognized that location of the sensors 12 a,b can bepositioned to either side of the centerline 50 running front to backrather than to either side of the centerline 56 running side to side (ofthe wearer), as the separation distance for the typical wearer isgreater side to side rather than front to back (i.e. wider between hipsverses between spine and belly button).

Further, one example option for the sensor configuration is a4-electrode ECG sensor configuration. Cost of such an ECG design can bea factors however the design could potentially give better signalperformance. The theory behind the four sensor ECG design is that theprocessor 16 can switch between each sensor pair (of the multiple pairECG sensor configuration) to find the one with the best signal qualityand use that one during sensed movement of the wearer.

Referring again to FIG. 3, the processor 16 and associated stored 18instructions can be used to determine (based on received sensor 12readings) bio impedance values by utilizing both of the ECG sensors 12 aand the sensors 12 b at the same time. This is advantageous as ECGsensing (using sensors 12 a) cannot occur at the same time as bioimpedance sensing (using sensors 12 b), as signal amplitude generated bythe sensors 12 b oversaturates the ECG sensors 12 a. As such, it isrecognized that the processor 16 cycles between ECG readings and bioimpedance readings (i.e. these readings are done sequentially ratherthan in parallel). As such, the processor instructs power to both thesensors 12 a,b on one side of the centerline 50 as drivers and both thesensors 12 a,b on the other side of the centerline 50 as collectorsduring taking of bio impedance readings. As such, it is recognized thatthe positioning of the sensor pair 12 a and the sensor pair 12 b can besymmetrical about the centerline(s) 50,56.

Referring to FIGS. 3 and 4, the computer device 14 can be used to sendthe sensed data 44 to the off band computer device 40, which can thenuse its own customized applications 43 to process the sensed data 44 toinform the wearer of their physical/mental state on potentialadaptations/changes that can be actively done by the wearer. Forexample, the application 43 can report sensed data 44 pertaining to acombination of temperature and activity over time as an indicator of thequality of sleep of the wearer. Further, the application 43 can notifythe wearer of a determined emotional state of the wearer (e.g. based ona combination of breathing data and activity data—with optional ECGdata) as well as continued monitoring of the data combination to informthe wearer whether steps taken by the wearer are positively influencingthe determined emotional state. Further, the application 43 can trackand report on the degree as well as quality/nature of the wearer'sactivity, for example based on a combination of strain gauge data andactivity data. Further, the application can interact with other externalcomputer networked devices 60 (see FIG. 3) such as but not limited tomusic systems, heating system, lighting systems, etc. in response to adetermined mood and/or temperature of the wearer based on a combinationof sensed data (e.g. activity, heartrate, etc.).

Referring to FIGS. 5 and 6, shown is an alternative embodiment of theband 10, in exploded view. In particular, the band 10 is composed of afront band portion 60 and a back band portion 62, such that the portion60 has sensors 12 a,b with communication pathways 30 electricallyconnecting the sensors 12 a,b to respective connectors 64 (which connectto respective connector portions of the PCB 26 (see FIG. 3), in order toelectrically couple the sensors 12 a,b to the network interface 20). Theband portion 62 has cutouts 66 in order for the sensors 12 a,b to bereceived in the cutouts 66 when the band portions 60,62 are assembledwith one another (e.g. coupled together for example by stitching viaadjacently places surfaces 70), thus providing for surfaces 68 of thesensors 12 a,b to become in contact with the skin of the wearer, as thesurface 111 is for contact with the skin. It is recognized that theelectrically conductive pathways 30 can be electrically conductivefibres interlaced with electrically insulative fibres comprising thematerial of the band portion 60.

Referring to FIG. 7, shown is an example side view of one of the sensors12 a,b, such that the portions 60,62 are assembled and the sensors 12a,b are received in the cutouts 66 (see FIGS. 5,6). It is important tonote that the sensors 12 a,b themselves extend from the skin contactsurface 111 by a distance X, thus providing for improved contact withthe skin of the wearer. In particular, the sensors 12 a,b can have aconductive portion 72 of the surface 68 (i.e. coupled to thecommunication pathways 30 extending through backing material 74) as wellas the raised backing material 74 to provide for the respectiveextension of the conductive portion 72 of the sensors 12 a,b from thesurface 111. For example, the backing material 74 can be comprised ofelectrically insulative interlaced fibres interleaved with the textilefibres incorporating the material (i.e. electrically insulative fibres)of the band portion 62.

Referring to FIG. 8, shown is a further embodiment of the band portion60 showing the strain gauge sensor 12 c woven/knit in a serpentinefashion with other insulative fibres comprising the material of the bandportion 60. As such, as shown in FIG. 7, it is recognized that onceassembled, the band portion 62 would cover the strain gauge sensor 12 cand thus insulate the skin of the wearer from direct contact with theelectrically conductive fibres of the strain sensor 12 c.FIG. 9 shows afurther geometrical configuration of the strain sensor 12 c.

Referring to FIGS. 5 to 8, it is recognized that they contain examplegeometrical layouts of the communication pathways 30 (e.g. traces) andthe strain sensor 12 c itself. The shown construction of the sensors 12a,b,c and band portions 60,62 are advantageous, as the entire pattern(of pathways 30 and sensor(s) 12 c) is actually contained withincovering portions 60,62 as one assembled (e.g. interlaced) layer offabric, however the traces (of pathways 30 and sensor(s) 12 c) areknitting inside the knit pattern and therefore as a consequence of thatare insulated, therefore inhibiting any necessity of external insulation(glues, laminates, etc.). in order to inhibit undesirably application ofelectrical charge from the traces to the skin of the wearer. Further,the 3D shape (e.g. extension from the surface 111) of the sensors 12 a,bthemselves can improves the sensors 12 a,b contact with the skin and canprovide for the collection of biometric data across a variety of skinconditions, dry or wet.

Referring to FIG. 10, shown is a garment application 100bi-directionally communicating over the network 22 with a plurality ofnetworked devices 15, each having a device application 102 capable ofsending and receiving data 44,45 (i.e. bidirectional) with the garmentapplication 100 via the network 22. It is recognized that the garmentapplication 100 receives biometric data 44 via the interface 20 (e.g.API) and then can send the commands 45 based on the data 44 (e.g. raw orotherwise processed) to one or more networked devices 60 in order toinfluence the operation of the networked device 60 via the deviceapplication 102 running on the device 60. For example, the deviceapplication 102 can be a thermostat application 102 running on a homethermostat 60 and thus able to instruct the thermostat 60 to raise orlower the temperature setting controlled by the thermostat, recognizingthat there are further bidirectional use cases described by examplebelow.

The garment application 100 receives the biometric data 44 collected bythe sensors 12,36 incorporated in the garment 11 (e.g. shirt,pants/shorts, vest, underclothing, hat, and/or any other garment typeincorporating the sensors 12,36 as part of or external to the band 10).The garment application 100 can interact with other external computernetworked devices 60 (see FIG. 10 such as but not limited to musicsystems devices 60, heating system devices 60, lighting system devices60, security system devices (e.g. locking/unlocking doors) and otherdevices 60 configured to interact with the wearer 8 of the garment 11via the garment application 100. It is recognized that the garmentapplication 100 can be one or more applications 100 running on one ormore computer platforms, for example such as but not limited to thegarment application 100 executing on the computer device 14, the garmentapplication 100 executing on the external device 40 (e.g. wearer'smobile device), and/or a cloud-based garment application 100 hosted on awearer account on a network server 41, as desired. In any event,regardless of the one or many differently hosted garment applications100, the garment application(s) 100 is/are configured to receive thebiometric data 44 collected from the sensors 12,36 by the computerprocessor 16, optionally process or otherwise analyze the biometric data44, compare the data 44 (i.e. raw or processed) against one or morestored thresholds or rule sets 45 (further described below), to generatea command 45 for instructing the device application 102 to modifyfunctional behavior(s) of the respective networked device 60, tocommunicate with the networked device 60 the command 45 as well asprovided responses 45 to the command from the networked device 60 inresponse to receiving the command 45. As further described below, thecommand 45 can be generated by the garment application 100 in responseto a determined mood and/or temperature of the wearer based on acombination of sensed data 44 (e.g. activity, heartrate, etc.).

Referring again to FIG. 10, a garment interaction service 101 can beimplemented on the server 41, for example, however it can also be inwhole or in part hosted on the external device 40, as desired. Thegarment interaction service 101 (see FIG. 11) contains a wearer account110 registered with the garment application 100, as well as respectivedevice accounts 112 registered with their respective device application102 of their networked device 60. The accounts 110,112 are registeredwith the service 101 prior to network 22 interaction there-between. Forexample, a wearer 8 wishing to control their home thermostat 60 andtheir home lighting system 60 and their home music system 60 (it isrecognized that one or more of these device 60 functions can becontrolled by the same device application 102, as desired, rather thanby separate device applications 102) can register with the interactionservice 101 as well as register the network device applications 102,thus creating accounts 110,112. Using the accounts 110,112, theinteraction service 101 can receive data 44, commands 45, and responses45, thereby acting as a third party server/service for use incoordinating the network 22 interaction between the garment 11 and thedevice 60.

The accounts 110,112 can contain registration information such as butnot limited to: wearer login and password account information, wearersettings information 114 for device 60 operation (e.g. desired device 60operation based on wearer parameter settings), device operation settings116 (e.g. permitted functionality accessible to modify based on receivedcommands 45), etc. For example, in terms of wearer settings information114, the wearer can specify music type selections (as played by musicsystem device 60) for different wearer moods such as but not limited to“easy listening” music for active but considered happy/content wearermood, “restful listening” music for use in calming the wearer duringrestful situations (e.g. sleep), “active listening” music for use inmotivating the wearer to become more physically active, etc. Othersettings 114 can include such as but not limited to: desired lightinglevels (as moderated by lighting system device 60) based on determinedwearer activity level/mental state, desired temperature settings (asmoderated by heating/cooling system device 60) based on determinedwearer activity level/mental state, operational mode of automobile (asmoderated by automotive system device 60) based on determined weareractivity level/mental state, and/or the garment 11 itself based onfunctional devices 60 resident on/in the garment 11 fabric such as butnot limited to actuators (e.g. electronic sensors capable of applying anelectrical/vibrational stimulus to the wearer, heating device capable ofapplying heat to the wearer, cooling device capable of removing heat orotherwise cooling the wearer, and/or any other device 60 that can changeits functional state based on receiving of the command 45 generatedusing sensed and processed (e.g. via application 100) biometric data 44.Another example of wearer settings information 114 is for locationsettings, such that the wearer can specify the definition of certainphysical locations (e.g. geolocation X represents the wearer's home,geolocation Y represents the wearer's work/employment, geolocation Zrepresents the wearer's preferred hobby, geolocation X1 represents thewearer's location within the home—e.g. bedroom, etc.). It is alsorecognized that the wearer settings information 114 can be used todefine the wearer's environment based on co-registration of the device14 with an adjacent device (e.g. pairing the device with the externaldevice 40 can be used to indicate when the wearer is exercising at theirgym, driving their car, etc.). As such, it is recognized that thegarment application 100 can also be informed of the wearer'sactivity/mental state based on information obtained from sensors/devices13 (e.g. current Bluetooth connectivity with another device 60 such asan automotive communication system, GPS sensors resident on the externaldevice 40, etc.).

In view of the above, it is recognized that the garment application 100is responsible for receiving the biometric data 44 on a periodic (e.g.determined regular frequency of data 44 reporting) basis and/or on arequested basis (e.g. in response to a command 45 generated, and sent tothe networked device 60 which in turn changes an operational state ofthe networked device 60). In this way, scheduled periodic and/or uponrequest, the garment application 100 can be used to monitor thephysical/mental state of the wearer 8 over a period of time, and asinstructed by the wearer settings 114, can adjust the operationalfunctionality of one or more of the networked devices 60 based onreceived and interpreted biometric data 44.

It is recognized that the garment application 100 can have access to aplurality of data models 109 for use in comparing a plurality ofbiometric data 44 from two or more different sensor types (e.g. activitysensor and temperature sensor, temperature sensor and ECG sensor,activity sensor and posture sensor, activity sensor and location sensor,etc.). The data models 109 each represent a series of data 44 valuecombinations, which define a particular desired (or undesired)physical/mental state of the wearer 8 (for example as defined by thewearer 8). For example, data 44 can comprise; 1) a location of the home(e.g. bedroom), a time of day (e.g. nighttime), a temperature reading(e.g. elevated), and an activity reading (e.g. wearer motion), 2) can bereceived by the garment application 11 and 3) compared to a data model109 representing a desired sleep pattern for the wearer 8. In the eventthat the data 44 matches the desired sleep pattern of the sleep datamodel 109, the garment application 100 would not generate any commands45 and thereby attempt to moderate or otherwise affect any networkeddevices 60 (e.g. thermostat 60, music system 60, etc.) associated withthe sleep data model 109.

As such, referring to FIG. 12 for command operation 200, the garmentapplication 100 compares 204 the biometric data 44 (as well as any otherdata provided by third party devices such as but not limited to theexternal device 40), comprising multiple data types collected/receivedfrom the sensors 12,36, to the data model(s) 109. For example, thegarment application 100 can be configured to receive periodically (e.g.every 10 seconds) data 44 from each of the sensors 12,36 of the garment11. In response to the received 202 data 44, the garment application 100can compare 204 the data 44 to each of the model(s) 109 and generate 206one or more commands 45 in the event the data 44 matches (or does notmatch) one or more of the data models 109. It is recognized that each ofthe data models 109 would have a set of instructions 111 (see FIG. 10)for use in determining/suggesting what action(s) is/are appropriate inthe event that the data 44 matches (or does not match), and to whatdegree, the data patterns implicit in the data model(s) 109 match or donot match the plurality of data 44 (of different data types) provided bythe sensors 12,36.

Sleep Example

One example of operation, following FIG. 12, of the garment application100 is for monitoring 200 a sleep or restful state of the wearer 8. Forexample, the garment 11 by way of the sensor 12,36 data received 202 bythe garment application 100 can indicate an activity level (e.g.accelerometer data 44) of the wearer 8, a temperature level (e.g.temperature sensor data 44) of the wearer 8, and a posture or bodyattitude level (e.g. strain sensor or gyroscopic data 44) of the wearer8. The garment application 100 can compare 204 these received data 44levels to one or more sleep patterns/thresholds of the sleep data model109 in order to determine 205 if the wearer 8 is having a sleep episodethat matches (e.g. representing a restful sleep) or does not match (e.g.represents a disturbed/fit full sleep) the sleep pattern(s) of the sleepdata model 109. At step 206, based on the degree of match or mismatch,the garment application 100 can generate 206 a command for one or moreof the networked devices 60 (as associated with the data mode 109 viathe instructions 111) and send 208 the command and receive feedback 45(e.g. an acknowledgement response, a response indicating a change ordegree of change in operational function of the networked device 60,etc.) from the networked device 60. In the case of the sleep example,the garment application 100 can generate 206 a decrease temperaturecommand 45 by a defined amount (e.g. by 2 degrees Centigrade), based onthe set of rules 111, and send 208 the command 45 to the thermostat 60.The garment application 100 can receive acknowledgement 45 of thetemperature decrease command from the thermostat 60 and can subsequentlymonitor 210 (e.g. via further programmed periodic or requested data)further data 44 of the wearer 8 to determine via a further data model109 comparison 212 whether the new/revised data 44 (a consequence of theissued command 45) represents a desired change (e.g. improvement) 213 inthe wearer's activity/mental state represented by the data model 109, orlack of improvement thereof. In the case of a desired change at step213, the garment application 100 would refrain from issuing furthercommands 45 to the networked device 60 and thus continue to monitor 202the wearer 8 via further periodic receipt of the data 44 and comparisonto the data model(s) 109. If the change/no change determined at step 213needs further commands 45 to be issued (e.g. sleep has improved but notto an acceptable level as represented in the model 109 data patterns),the garment application 100 returns to step 206.

In the above example, one potential data pattern of the sleep data model109 is where the wearer's temperature is elevated (e.g. wearer is toohot) and the wearer's activity/motion level is also elevated (e.g.wearer is tossing and turning). The command 45 issued would be todecrease the room temperature to the thermostat and the garmentapplication 100 would monitor the effect of the temperature change, e.g.a lowering of the wearer temperature. Subsequent monitored lowering ofthe wearer activity level via the new data 44 to acceptable levels asdefined in the sleep data model 109 would return the garment applicationto operating at step 202. On the contrary, subsequent monitoredraising/unchanged of the wearer activity level via the new data 44representing non-acceptable levels as defined in the sleep data model109 would return the garment application to operating at step 206, in aneffort to continued lowering of the room temperature in order tofacilitate a decrease in the wearer's body temperature and/or activitylevel.

Mental State Example

It is recognized that the number of potential applications for thegarment 11 paired with the garment application 100 and the deviceapplication(s) 102 can be numerous. A further example is where thegarment application 100 detects (i.e. via the sensed data 44) anelevated heart rate (still with acceptable norms—i.e. not indicative ofa heart attack) without a corresponding increase in physical activitylevel. This physical state of the wearer, as defined/matching a datamodel 109, could be indicative of an anxiety attack. In this case, thegarment application 100 could be programmed via the instructions 111 ofthe data model 109 to instruct a networked device 60 such as a musicsystem 60 to play restful/meditative music. Continued monitoring of thephysical state by the garment application 100 could be used to determineby the garment application 100 if the commanded 45 changes to theoperational/functional state of the networked device 60 are having anyeffect on the wearer's physical/mental state.

It is recognized that the data model 109 by way of the instructions anddata patterns 111 can be used to define more complex state(s) of thewearer 8, via a combination of a plurality of the various sensor 12,36types and their data. For example, the current mental state (e.g. happy,sad, anxious, excited, sedate, depressed, relaxed, etc.) can bedetermined as a result of a combination of the plurality of sensed data44 matching (or not matching) the data model(s) 109 representing thatmental state. For example, the data 44 for heart rate, temperature,activity level, and posture can be used, as a combination, to define andpredict the current mental state of the wearer 8, based on the mentalstate modelling as represented by a mental state data model 109.

Notification Emergency Example

It is also recognized that in the event that the operation 200, as shownin FIG. 12, does not mitigate or otherwise obviate the determinedmatch/mismatch of the data model(s) 109 performed by the garmentapplication 100 using the sensed data 44 (i.e. as determined via thecomparisons with the data model 109), the garment application 100 couldbe programmed via the settings 114 to send a notification 50 to aspecified device 52 indicating a potential emergency/crisis event. Forexample, this specified device 50 could be that of a family member,medical practitioner, notification service, or friend, which wouldreceive the notification 52 and could be informed of the wearer'sactivity/mental state and/or otherwise encouraged to perform some action(e.g. contact the wearer 8, contact a medical practitioner, etc.)—seeFIG. 10. The device 52 could also be the external device 40 of thewearer 8, thus providing the wearer 8 with direct indication of theirsituation (e.g. “you are too excited and maybe you need to calm down?”).

It is also recognized that the operation 200 could be used to determinean actual considered detrimental/emergency condition of the wearer 8,e.g. heart attack, car accident or other body trauma, kidnapping, etc.,such that the data models 109 are used to indicate/determine (by thegarment application 100 comparing the data 44 to the rules and datapatterns 111 of the data model 109) that the data 44 is well outside (orinside) expected norms/thresholds defined in the data models 109. Forexample, the data 44 when compared to the data models 109 could indicatea heart attack (e.g. via ECG readings 44 and activity readings 44), astroke (e.g. EGC readings 44 and activity level readings 44), kidnapping(e.g. anxiety level readings 44, activity level readings 44 andlocation/change in location readings 44), etc.

Mental/Physical Activity Example

A further example operation 200 can be for a planned physical activity(e.g. cycling, jogging) of the individual wearer 8. The data model 109representing the physical activity can be used by the garmentapplication 100 to monitor the wearer's biometric data 44, and to reportto the wearer 8 via the computer device 14 (e.g. sound, light or otherhaptic commands/sensations) and/or via the external device 40 (e.g.sound and/or messages on a screen of the device 40) suggestions to thewear 8 while performing the activity. For example, hydration levels(e.g. physical state) of the wearer 8 can be monitored by the garmentapplication 100, via the sensed data 44 and comparison to the datamodel(s) 109 representing the activity, and thus a notification (e.g.command 45) can be sent to the wearer 8 (i.e. via the device 14,40)indicating that hydration levels are outside of a threshold (e.g. toolow) and thus the wearer 8 should correct (e.g. hydrate by drinking).Again, as per the operation 200 described above, the dynamic physicalstate of the wearer 8 would be continually monitored by the garmentapplication 100 (in comparison of data 44 with the data model 109) andthus further suggestions (e.g. of hydration) would be sent to the wearer8. Alternatively, a notification 45 of the detected physical state (e.g.hydration) back within accepted norms could be sent to the wearer 8 as aconsequence of the continued monitoring.

A further example operation 200 can be for a planned physical activity(e.g. cycling, jogging) of the individual wearer 8. The data model 109representing the mental activity/state can be used by the garmentapplication 100 to monitor the wearer's biometric data 44, and to reportto the wearer 8 via the computer device 14 (e.g. sound, light or otherhaptic commands/sensations) and/or via the external device 40 (e.g.sound and/or messages on a screen of the device 40) suggestions to thewear 8 while performing the activity. For example, focus levels (e.g.mental state) of the wearer 8 can be monitored by the garmentapplication 100, via the sensed data 44 and comparison to the datamodel(s) 109 representing the activity (for example as a result ofmonitored body posture, breathing rate, heart rate, etc.), and thus anotification (e.g. command 45) can be sent to the wearer 8 (i.e. via thedevice 14,40) indicating that focus levels are outside of a threshold(e.g. too low) and thus the wearer 8 should correct (e.g. refocus).Again, as per the operation 200 described above, the dynamic mentalstate of the wearer 8 would be continually monitored by the garmentapplication 100 (in comparison of data 44 with the data model 109) andthus further suggestions (e.g. of refocus) 45 would be sent to thewearer 8. Alternatively, a notification 45 of the detected mental state(e.g. focus) back within accepted norms could be sent to the wearer 8 asa consequence of the continued monitoring.

It is also recognized that the data model(s) 109 could be used to detectthe type of physical activity being performed by the wearer 8 (e.g.yoga, cycling, etc.), based on the sensed data 44 matching a particularactivity type pattern. Once detected, the garment application 100 couldselect an use an appropriate data model 109 representative of thedetected activity type to monitor the state (e.g. physical/mental) ofthe wearer 8 as the activity is being performed. The physical activitycan be an activity such as but not limited to; vigorous physicalactivity such as a physical sport (e.g. cycling, running, weighttraining, etc.) non-vigorous physical activity/sport (e.g. dartthrowing, yoga, tai chi, etc.); active/concentrated mental activity suchas computer work at the wearer's place of employment; relaxed mentalactivity such as reading/relaxation/listening to music/meditation; etc.In any event, it is recognized that the data models 109 can be used tooptionally detect and to also monitor the physical/mental activity ofthe wearer 8, based on the sensed data 44 in comparison to the requisitedata model(s) 109 as discussed above with respect to the operation 200.

Also provided for is an example of a command flow wherein othernetworked devices 60 (or their associated applications 102, or even 3rdparty applications 102) subscribe to the biometric data 44, for exampleprovided by the service 101 over the network 22. The devices 60 wouldreceive the data 44 from the service 101 (or directly from the wearer 8via the devices 14,40) and apply decision making criteria (e.g. similarto comparison of the data 44 with data model(s) 109 as described above)in order to make a determination about what action (e.g. sendingnotifications/communications/commands 45, etc. to the wearer 8 or toother third party devices over the network 22) to take based on thecomparison.

We claim:
 1. A method of using a sensor platform of a garment of awearer in order to interact with a remote networked device using aplurality of sensed biometric data, the method comprising: receivingfrom the sensors a set of the plurality of biometric data; comparing theset to a data model including a plurality of model data parameters;determining whether said comparing indicates a need for a command to besent to the remote networked device in order to effect a change in anoperational characteristic of the networked device; sending the commandto the networked device; receiving a further set of the plurality ofbiometric data; further comparing the further set to the data model; anddetermining whether said further comparing indicates a need for afurther command to be sent to the remote networked device in order tofurther effect a change in an operational characteristic of thenetworked device.
 2. The method of claim 1, wherein the data modelrepresents state selected from the group consisting of: a physical and amental state.
 3. The method of claim 1, wherein the garment isconfigured for wearing next to the skin of the wearer.
 4. A method ofprocessing a plurality of sensed biometric data of a sensor platform ofa garment of a wearer device, the method comprising: receiving a set ofthe plurality of biometric data; comparing the set to a data modelincluding a plurality of model data parameters; determining whether saidcomparing indicates a need for a communication to be sent to the weareror to another networked device; sending the communication to thenetworked device; receiving a further set of the plurality of biometricdata; further comparing the further set to the data model; anddetermining whether said further comparing indicates a need for afurther communication.